Seasonal affective disorder (SAD) occurs in two main forms: winter-pattern SAD, associated with depressive symptoms during shorter, darker days; and summer-pattern SAD, linked to mood disturbances during longer, hotter days. SAD may develop into a chronic condition with recurring depressive episodes. Risk factors for SAD include geographic latitude, age, gender, genetic predisposition, and lifestyle. Sleep disturbances, such as insomnia, hypersomnia, and circadian rhythm disruptions, are common and can amplify emotional symptoms.

This review explores the clinical features and management strategies for insomnia associated with SAD, focusing on the potential of benzodiazepines (BZDs), in particular lormetazepam.

Controversies surround current nonpharmacological and pharmacological strategies for managing sleep disorders in SAD. This review emphasizes the importance of using more effective treatments for insomnia associated with SAD, currently an unmet need. In particular, clinical evidence supports the potential benefits of intermittent hypnotic BZDs to treat insomnia. Among the BZDs, short-term or intermittent use of lormetazepam is an effective treatment option in the management of insomnia.

Insomnia associated with SAD is an important symptom to monitor because it impacts the patient's quality of life. BZDs, including lormetazepam, are a standard short-treatment option for insomnia that could improve the sleep symptoms associated with SAD. Comparative clinical trials of the efficacy and safety of lormetazepam in this patient population are required to confirm this.

Low self-esteem (LoST) is a latent yet critical psychosocial risk factor that predisposes individuals to depressive disorders. Although structured tools exist to assess self-esteem, their limited clinical adoption suggests that relevant indicators of LoST remain buried within unstructured clinical narratives. The scarcity of annotated clinical notes impedes the development of natural language processing (NLP) models for its detection. Manual chart reviews are labor-intensive and large language model (LLM)-driven (weak) labeling raises privacy concerns. Past studies demonstrate that NLP models trained on LLM-generated synthetic clinical notes achieve performance comparable to, and sometimes better than those trained on real notes. This highlights synthetic data's utility for augmenting scarce clinical corpora while reducing privacy concerns. Prior efforts have leveraged social media data, such as Reddit, to identify linguistic markers of low self-esteem; however, the linguistic and contextual divergence between social media and clinical text limits the generalizability of these models. To address this gap, we present a novel framework that generates context-enhanced synthetic clinical notes from social media narratives and evaluates the utility of small language models for identifying expressions of low self-esteem. Our approach includes a mixed-method evaluation framework: (i) structure analysis, (ii) readability analysis, (iii) linguistic diversity, and (iv) contextual fidelity of LoST cues in source Reddit posts and synthetic notes. This work offers a scalable, privacy-preserving solution for synthetic data generation for early detection of psychosocial risks such as LoST and demonstrates a pathway for translating mental health signals in clinical notes into clinically actionable insights, thereby identifying patients at risk.

To identify specific neurocognitive risk markers in first-episode major depressive disorder (MDD) patients with positive family history (PFH).

Antipsychotic-naive adults aged 18-60 were recruited across three groups: major depressive disorder patients with positive family history (PFH-MDD, n = 171), major depressive disorder patients with negative family history (NFH-MDD, n = 185), and healthy controls (HCs, n = 180). All patients met the DSM-5 criteria for first-episode MDD (HAMD-24 ≥ 17). Neurocognition was assessed with the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS). Group differences were examined using the Kruskal-Wallis test and ANCOVA. Logistic regressions identified independent cognitive predictors; ROC curves evaluated discriminative validity.

The RBANS total and domain scores differed across the groups (p < 0.001). PFH-MDD performed worse than NFH-MDD in language function (p < 0.001) and total score (p < 0.001). In the PFH group, language function score was negatively correlated with HAMD score (r = -0.184, p = 0.016). In the NFH group, language function score was positively correlated with HAMA score (r = 0.402, p < 0.001) and negatively correlated with HAMD score (r = -0.364, p < 0.001). Total score was negatively correlated with HAMD score (r = -0.158, p = 0.032). After adjustment, language function (OR = 0.82, p = 0.042) and total score (OR = 0.90, p < 0.001) independently predicted PFH-MDD; only total score predicted NFH-MDD (OR = 0.77, p < 0.001). The ROC-AUC values for PFH-MDD were as follows: language = 0.967 and total score = 0.991. Gender × group interactions were non-significant.

Language dysfunction and global cognitive impairment may be independent markers of first-episode MDD with PFH. Early cognitive profiling may facilitate targeted prevention in high-risk relatives.

Breast cancer and its treatment may contribute to an increased risk of unemployment, influenced by both disease-related factors and socioeconomic determinant. Few longitudinal studies have examined employment outcomes among women diagnosed with cancer. This retrospective study investigated long-term employment among breast cancer survivors (BCS) and assessed disease specific and socioeconomic factors associated with employment.

Registry-based data included working age BCS in Norway 2004-2008 alive at 6 years follow-up (N = 3560). The employment status on each BCS was compared to two matched non-cancer controls (N = 7081) by means of logistic regression analyses with marginal effects. Separate analyses by employment status at the time of diagnosis were conducted.

Among BCS employed at diagnosis, 73.7%, 71.5% and 71.8% of BCS were in employment at 1, 2 and 6 years after diagnosis, respectively. BCS employed at diagnosis had significantly lower probability of being employed at all follow-up time points, compared to controls. BCS outside employment at the time of diagnosis experienced lower probability of employment compared to controls. BCS with secondary or higher education had higher probability of employment compared to BCS with basic education, and BCS living in families with children were more likely to enter employment during follow-up compared to BCS without children.

BCS employed at diagnosis had a subsequent risk of unemployment, and BCS not employed at diagnosis had lower probability of entering employment. Additional risk factors are high age, low education, and being single without children.

The risk of unemployment after a breast cancer diagnosis was increased. Job loss is costly economically and socially, both for individuals and for society. Early focus on employment particularly among employees with low education and with little family support may alleviate this problem.

Childhood, adolescence, and emerging adulthood represent critical transitional periods characterized by rapid biological, psychological, and social development, each of which may distinctly influence diet-mood interactions. To date, no study has concurrently examined the association between dietary diversity and depressive symptoms across the full developmental spectrum spanning these life stages. As a result, age-specific vulnerabilities and potential windows for intervention remain poorly understood. Using a large and diverse sample of Chinese children, adolescents, and university students, this cross-sectional study aimed to explore the relationship between dietary diversity and depressive symptoms across these key developmental periods. The findings may help inform the design of targeted, developmentally appropriate nutritional strategies for depression prevention.

In this cross-sectional investigation, a total of 11,856 Chinese college students and 1,281 children and adolescents were enrolled. All participants completed self-administered questionnaires assessing demographic characteristics, lifestyle behaviors, dietary diversity, and depressive symptoms [evaluated with the Patient Health Questionnaire-9 (PHQ-9) and the 20-item Zung Self-Rating Depression Scale (SDS)]. Multivariable logistic regression analyses were employed to examine the associations between dietary diversity and depressive symptoms, with adjustment for relevant confounding factors.

The prevalence of depressive symptoms was 18.9% (2,245/11,856) among college students and 4.7% (60/1,281) among children and adolescents. Among college students, a significant inverse relationship was observed between dietary diversity scores and depressive symptoms. Compared to participants with a score of 0, the adjusted odds ratios decreased progressively with higher scores, ranging from OR = 0.94 (95% CI: 0.39, 2.30) for a score of 1 to OR = 0.33 (95% CI: 0.13, 0.81) for a score of 9. Similarly, among children and adolescents, higher dietary diversity was associated with markedly lower odds of depressive symptoms, with ORs declining from 0.164 (95% CI: 0.007, 3.837) for one food score to 0.026 (95% CI: 0.002, 0.390) for seven food scores, relative to the zero-score reference group. In analyses of specific food groups, college students showed significant inverse associations between depressive symptoms and consumption of vegetables (OR = 0.67, 95% CI: 0.55, 0.81), fruits (OR = 0.78, 95% CI: 0.70, 0.88), red meat (OR = 0.85, 95% CI: 0.75, 0.95), and soy products (OR = 0.89, 95% CI: 0.80, 0.99). Among children and adolescents, significant associations were observed for multiple dietary factors, with inverse associations for fruit intake (P = 0.019) and breakfast consumption (P < 0.001), and positive associations for sugar-sweetened beverages (P = 0.025), fried foods (P < 0.001), fast food (P < 0.001), and processed foods (P = 0.033).

This study establishes a significant inverse relationship between dietary diversity and depressive symptoms. The results support the integration of dietary diversity into public health recommendations and behavioral interventions. Specifically, fostering diverse and healthy eating patterns emerges as a promising, practical strategy for the prevention of depressive symptoms, underscoring the role of nutrition in mental well-being.

Early identification of individuals at risk for cognitive impairment is essential for timely intervention and public health planning. While sociodemographic and clinical predictors are well recognized, the role of nutrition and its interactions in cognitive health remains less explored.

Using data from the 2011-2014 National Health and Nutrition Examination Survey (NHANES, n = 2,208), we developed ensemble machine learning models (LightGBM, XGBoost, Random Forest) to predict cognitive impairment across three neuropsychological assessments (CERAD-WL, DSST, AFT). SHapley Additive exPlanations (SHAP) were applied to quantify and interpret the contribution of demographic, clinical, and nutritional predictors, as well as their interactions. To validate the nutrient interactions identified by our models, we conducted exploratory in vitro experiments assessing oxidative stress and neuroprotective pathways in SH-SY5Y neuronal cells.

Ensemble models demonstrated excellent predictive performance, consistently outperforming traditional classifiers. Key predictors included education, age, socioeconomic status, and chronic disease conditions. Among nutritional factors, vitamin B2 emerged as consistently associated with lower predicted cognitive impairment risk across all three models, with notable interactions observed with copper and vitamin E. Exploratory in vitro experiments supported these associations, showing reduced oxidative stress and increased expression of neuroprotective genes (SIRT1, BDNF) under vitamin B2 treatment, particularly when combined with copper or vitamin E.

Interpretable machine learning models integrating cognitive tests with demographic, clinical, and nutritional variables can accurately predict cognitive impairment. Nutritional predictors, particularly vitamin B2 and its interactions, may contribute to model performance and biological plausibility, suggesting potential avenues for stratified monitoring strategies.

Professor Rinaldo Bellomo was a pioneer in intensive care medicine whose influence extended beyond clinical trials to preclinical and translational research. His long-standing collaboration with the Florey Institute established ovine sepsis models that overturned dogma, demonstrating that septic acute kidney injury can occur despite increased renal blood flow, and clarifying the effects of fluids, noradrenaline, vasopressin, and angiotensin II. These studies advanced understanding of renal medullary hypoperfusion and hypoxia, guided antioxidant strategies, and inspired clinical investigation of megadose sodium ascorbate. Rinaldo also championed integration of preclinical science into the ANZICS Clinical Trials Group, legitimising dedicated sessions, fostering collaboration, and ensuring basic discoveries informed hypothesis-driven clinical trials. A gifted mentor and communicator, he inspired generations of researchers with his curiosity, humility, and passion for physiology. His legacy as both clinician and scientist underscores the enduring value of discovery research in advancing critical care.

Professor Rinaldo Bellomo's lasting impact on critical care research stems from his commitment to structured, biologically grounded research programs over isolated studies. His work on vitamin C in sepsis exemplifies this approach. While early enthusiasm grew around combination therapies involving vitamin C, Rinaldo championed a cautious, rigorous, and methodical investigation. He worked closely with collaborators to address key methodological issues, including dosing, stability, and the design of appropriate control groups, which ultimately led to the international VITAMINS trial. This landmark study compared vitamin C, hydrocortisone, and thiamine to hydrocortisone alone in septic shock and found no clinical benefit. Rinaldo embedded a pharmacokinetic substudy to confirm supraphysiological serum vitamin C levels, ensuring biological plausibility of the trial design. Beyond clinical research, he fostered translational research with the Florey Institute using a preclinical sheep model of sepsis. This collaboration uncovered critical mechanisms of septic acute kidney injury and led to the development of mega-dose sodium ascorbate therapy. The program progressed from proof-of-concept to a double-blind pilot randomised trial in septic shock and now underpins a national multicentre phase Ib and II clinical trials. Rinaldo's legacy is defined by scientific rigour, mentorship, and humility. His visionary, disciplined approach remains a model for impactful research and continues to guide ongoing efforts to advance care for critically ill patients.

This article reflects key themes and discussions from the American Epilepsy Society Annual Meeting 2025, Epilepsy and Aging Special Interest Group (SIG) session entitled "Multimodal Biomarkers of Epilepsy in Older Adults." The perspectives presented here are intended to highlight emerging priorities for the field. Epilepsy in older adults is the fastest-growing segment of the epilepsy population worldwide. Despite rising incidence, prevalence, and substantial morbidity, care for late-onset epilepsy (LOE) remains anchored to a seizure-centric framework that inadequately addresses the broader consequences of seizures in later life. Older adults with LOE face markedly increased risks of dementia, mortality, and stroke, yet are frequently excluded from epilepsy and Alzheimer's disease (AD) clinical trials. Patient-centered outcomes, including cognition, sleep, function, and quality of life, remain underprioritized. In this article, we argue that LOE requires multimodal biomarkers and multidisciplinary care. We contend that LOE should be reframed as a biologically meaningful warning signal of network vulnerability and overlapping brain pathology, rather than a late-life complication to be managed pragmatically. Cognitive dysfunction is common, heterogeneous, and often precedes overt neurodegenerative diagnoses, positioning cognition as an early clinical signal. Neuroimaging and electrophysiological evidence further place LOE along a continuum intersecting cardiovascular risk factors, sleep disruption, and AD biology, challenging traditional silos between epilepsy and dementia care. We argue for greater inclusion of older adults in antiseizure medication trials and for the inclusion of individuals with epilepsy in AD clinical trials. We propose a brain-health-centered framework for LOE that integrates longitudinal electroencephalography, particularly sleep-inclusive strategies, routine cognitive screening with targeted neuropsychological assessment, neuroimaging, vascular and sleep risk evaluation, and selective use of neurodegenerative biomarkers when clinically actionable. Together, these shifts move care beyond seizure counting toward a comprehensive brain-health model aligned with the realities of aging epilepsy.

Insomnia, a prevalent sleep disorder, adversely impacts patients' quality of life and imposes significant burdens on both physical and mental health. While conventional insomnia therapies remain widely utilized, they exhibit persistent safety limitations, including risks of dependence and cognitive impairment. Natural products have garnered increasing scientific interest owing to their favorable safety profiles and demonstrated therapeutic efficacy. This comprehensive review critically examines contemporary advances in understanding the mechanistic actions of natural products against insomnia and their supporting clinical evidence, with the objective of synthesizing their pharmacological mechanisms, clinical effectiveness, and safety evaluations.

This systematic review retrieved relevant literature through comprehensive searches of four core biomedical databases (ScienceDirect, PubMed, Ovid MEDLINE, and Web of Science), encompassing peer-reviewed articles published between January 2020 and August 2025. The retrieval strategy focuses on identifying original research papers concerning the treatment of insomnia using purified natural products and traditional Chinese medicine compound formulations.

Natural products demonstrate significant therapeutic efficacy against insomnia through multi-target mechanisms, including: modulation of GABAergic neurotransmission, regulation of orexinergic signaling pathways, attenuation of inflammatory responses and oxidative stress, restructuring gut microbiota composition, and normalization of core circadian regulators (notably CLOCK/BMAL1 complexes). Critically, clinical evidence confirms these natural products treatment outcomes with favorable safety profiles in insomnia management.

This review critically evaluates contemporary advances in botanical therapeutics for insomnia management, synthesizing evidence to inform evidence-based clinical translation and facilitate the development of novel pharmacotherapeutic strategies.